Attention-based neural models [1]}, [2]}, [3]}, [4]} have proven successful for solving the RC task. These models use attention mechanisms to focus on those parts of a passage which have higher similarity to the question. Recently, models based on contextualized word representations such as BERT [5]} also achieved human-level performance on the SQuAD RC task. However, these attention-based models do not perform well when multiple documents from different sources need to be used to find the answer. [6]}, [7]}, [8]}, and [9]} proposed state-based reasoning models to solve the multi-hop tasks across multiple documents. These state-based models are closer to the attention-based RC models with an additional `state' representation that is updated iteratively. The `state' representations of a model allow it to focus on different parts of multiple documents during each iteration and then combine the information across multiple documents. [10]}, [11]}, and [12]} proposed graph-based models to solve this task. These models create an entity graph of the entities present in the documents and learn their representations using a convolutional neural network (CNN) or recurrent neural network (RNN). They use reasoning over this entity graph to find the answer to a given question. [13]} proposed a hierarchical graph network for multi-hop QA. They created three layers of graphs in their model: entity graph, sentence graph, and passage graph. Finally, a graph reasoning module was used to find the answer span from the passages. [14]} proposed a heterogeneous document-entity (HDE) graph model for multi-hop QA. Their graph contains different types of heterogeneous nodes such as document nodes, candidate answer nodes, and entity nodes. Edges connecting different types of nodes are treated differently in the graph. They also used graph-based reasoning over their HDE graph to find the correct answer. [15]} used an explicit path-based reasoning model across multiple documents for the multi-hop QA task. They constructed the chain of documents using common entities between documents where each chain led to a possible candidate answer. Each document chain may contain multiple entity-paths from the start document to the end document in the chain. They used an attention network to assign a score to each such entity path and finally assigned a score to each document chain. These scores were used to rank the candidate answers. This path-based reasoning can provide interpretable explanations for the multi-hop QA task. Similar to [15]}, [17]} proposed a path-based reasoning approach for this task. They created an entity graph using the entities present in documents and used graph convolutional network over this graph and the entity paths to find the answer.
